Systems and Methods for Dynamic Query Optimization for Distributed Computing Environments

The present disclosure generally relates to distributed computing systems, and more particularly to methods and systems for dynamically optimizing query execution in distributed computing environments

In distributed computing environments, efficient query execution is crucial for optimal system performance and resource utilization. Traditional query execution methods often face challenges in adapting to varying workloads and resource availability. One significant challenge is the static allocation of computational resources for query execution. This approach can lead to inefficiencies where queries are either under-provisioned, resulting in slow execution times, or over-provisioned, leading to wasted resources. Another issue is the lack of real-time adaptation to changing system conditions. As workloads fluctuate and resource availability shifts, static execution plans can become suboptimal, negatively impacting overall system performance. Furthermore, traditional systems often struggle with balancing workloads across multiple execution environments. This can result in some environments being overloaded while others remain underutilized, leading to inefficient resource allocation and potential bottlenecks. These challenges highlight the need for a more dynamic and adaptive approach to query execution in distributed computing environments.

Accordingly, there is a need for methods and systems for dynamically optimizing query execution in distributed computing environments. According to some embodiments, the system described herein may analyze queries, and/or perform real-time resource allocation and adaptive execution environment selection, to enhance performance and efficiency.

In one aspect, a method is provided for dynamically optimizing query execution in a distributed computing environment. The method may include receiving a query for execution; analyzing the query to determine its complexity. The analysis may include generating an execution plan for the query, determining a total data volume to be processed, and/or identifying types and number of operations to be performed. The method may also include determining required computational resources based on the analysis. The determination may include estimating a number of processing units needed based on the total data volume, estimating additional processing units needed based on the types and number of operations, and/or calculating a total number of processing units required; selecting an execution environment for the query. The selection may include identifying available execution environments capable of providing the required computational resources, determining current workload status of the identified execution environments, and/or choosing an execution environment with the lowest current workload, based on real-time workload information. The method may also include, in accordance with a determination that (i) no suitable environment is available or (ii) all suitable environments exceed a predefined workload threshold, provisioning a new execution environment. The method may also include submitting the query to the selected or provisioned execution environment; monitoring the query execution until completion. The method may also include updating a query performance database with execution statistics.

In some embodiments, the method may further include load balancing across multiple execution environments to optimize overall system performance.

In some embodiments, the distributed computing environment may include a cloud-based data warehousing system with separated compute and storage architectures.

In some embodiments, provisioning a new execution environment may include either creating a new compute cluster or scaling an existing compute cluster according to specific scaling mechanisms of the distributed computing environment.

In some embodiments, the method may further include repeating receiving the query, analyzing the query, determining the required computational resources, selecting the execution environment, submitting the query, monitoring the query execution, and/or updating the query performance database, for multiple queries in parallel. Each query's execution environment selection may be based on real-time workload information.

In some embodiments, the parallel processing of multiple queries may include dynamically adjusting execution environment selections based on changing workload conditions.

In some embodiments, dynamically adjusting execution environment selections may be based on platform-specific concurrency limits of the distributed computing environment.

In some embodiments, the distributed computing environment may be a cloud-based data warehousing system.

In some embodiments, the execution environments may be virtual warehouses with varying computational capacities.

In some embodiments, the analysis of the query may include examining statistics from previous executions of similar queries.

In some embodiments, the required computational resources may be determined based on a mapping of data volume and operation complexity to processing units.

In some embodiments, the provisioning of a new execution environment may be performed automatically without user intervention.

In some embodiments, the automatic provisioning of a new execution environment may be performed in accordance with the specific scaling mechanisms of the distributed computing environment.

In some embodiments, the monitoring of query execution may include collecting performance metrics for future optimization.

In some embodiments, performance metrics collected during query execution monitoring may be specific to the distributed computing environment.

In some embodiments, analyzing the query to determine its complexity may further include considering data distribution strategies and sort keys specific to the distributed computing environment.

In some embodiments, determining required computational resources may further include considering the impact of data distribution strategies on resource requirements.

In some embodiments, selecting an execution environment for the query may further include considering platform-specific workload management features of the distributed computing environment.

In some embodiments, the execution environments may be one of: virtual warehouses, compute clusters, or parallel processing units with varying computational capacities.

In some embodiments, the method may further include load balancing across multiple execution environments to optimize overall system performance, which may take into account platform-specific workload management capabilities of the distributed computing environment.

In some embodiments, the distributed computing environment may be one of: a multi-cluster shared data architecture, a massively parallel processing (MPP) system, or a distributed SQL query engine.

In some embodiments, the required computational resources are determined based on a mapping of data volume and operation complexity to platform-specific compute units of the distributed computing environment.

In another aspect, a computer system for dynamically optimizing query execution in a distributed computing environment is provided. The computer system may include one or more processors and a memory. The memory stores one or more programs configured for execution by the one or more processors. The one or more programs may include instructions for performing any of the methods described herein.

Like reference numerals refer to corresponding parts throughout the drawings.

Reference will now be made to various implementations, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention and the described implementations. However, the invention may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the implementations.

The development of the techniques described herein is a response to the inefficiencies and challenges faced in traditional query execution methods in distributed computing environments. These traditional methods often employ static resource allocation and lack real-time adaptation capabilities, leading to suboptimal performance and resource utilization. The present disclosure may address these challenges by introducing a dynamic and adaptive approach to query execution optimization. This approach leverages intelligent query analysis, real-time resource allocation, and adaptive execution environment selection to enhance system performance and efficiency.

An aspect described herein is the ability to analyze incoming queries in real-time to determine their complexity. This analysis takes into account factors such as the total data volume to be processed and the types and number of operations to be performed. By generating an execution plan based on this analysis, the system can make informed decisions about resource requirements. Another aspect is the dynamic determination of required computational resources. Instead of, or in addition to, relying on static allocations, the system may estimate the number of processing units needed based on the query's characteristics. This may include considering both the data volume and the complexity of operations, ensuring that resources are allocated efficiently.

Also described herein is a method for selecting an appropriate execution environment. By identifying available environments capable of providing the required resources and considering their current workload status, the system can choose the optimal environment for each query. This real-time selection process helps balance workloads across the distributed system and prevents bottlenecks. Furthermore, the system described herein may incorporate a provisioning mechanism that automatically creates new execution environments when existing ones are unavailable or overloaded. This ensures that queries can always be executed, even during peak demand periods, without manual intervention. The system may also include continuous monitoring of query execution and performance data collection. This information may be used to update a query performance database, which can be leveraged for future optimizations and decision-making. By addressing these aspects, the present disclosure offers a comprehensive solution to the challenges of query execution in distributed computing environments. It provides a flexible, efficient, and scalable approach that can adapt to changing workloads and system conditions in real-time.

1 FIG. 100 102 102 110 105 110 112 114 116 118 120 120 102 110 112 114 116 118 110 a n a n a shows an example distributed computing environmentimplementing the dynamic query execution optimization system, according to some embodiments. The environment may include multiple client devices-connected to a distributed computing systemvia a network. The distributed computing systemmay include a query reception module, a query analysis module, a resource allocation module, an execution environment selector, and/or multiple execution environments-. After a client device, such as client device, sends a query to the distributed computing system, the query may be received by the query reception module. The query analysis modulemay then analyze the query to determine its complexity, generating an execution plan and identifying the data volume and operations involved. Based on this analysis, the resource allocation modulemay determine the required computational resources. The execution environment selectormay then identify available execution environments capable of handling the query and/or select the most appropriate one based on current workload information. If no suitable environment is available or all environments are overloaded, the system can provision a new execution environment. The query may be submitted to the selected or newly provisioned environment for execution. During the execution process, the systemmay monitor performance and/or collect statistics, which may be used to update a query performance database for future optimization.

2 FIG. 1 FIG. 200 200 110 shows a flowchart of an example methodfor dynamically optimizing query execution in a distributed computing environment, according to some embodiments. The methodmay be performed by one or more computing devices (e.g., the distributed computing systemdescribed in).

200 202 204 The methodmay begin at stepwith receiving a query for execution. At step, the query may be analyzed to determine its complexity. This analysis may include generating an execution plan for the query, determining the total data volume to be processed, and/or identifying the types and number of operations to be performed.

206 At step, the method determines the required computational resources based on the analysis. This involves estimating the number of processing units needed based on the total data volume and the types and number of operations, and then calculating the total number of processing units required.

208 Stepmay include selecting an execution environment for the query. This selection process includes identifying available execution environments capable of providing the required computational resources, determining the current workload status of these environments, and choosing the environment with the lowest current workload based on real-time information.

210 212 210 Stepmay determine if no suitable environment is available or all suitable environments exceed a predefined workload threshold. In step, the system may provision a new execution environment, if stepdetermines that either no suitable environment is available or all suitable environments exceed a predefined workload threshold.

214 216 At step, the query may be submitted to the selected or provisioned execution environment. The query execution may be then monitored (e.g., until completion) at step.

218 Finally, at step, a query performance database may be updated with execution statistics. This information can be used for future query optimization and resource allocation decisions. The query performance database may be structured as a table that captures key metrics for each query execution. This table may include fields for a query identifier, execution time, amount of data scanned, CPU time used, memory usage, disk I/O operations, warehouse size used, concurrency level at execution time, timestamp of execution, and/or the execution plan. This comprehensive data collection may allow for detailed analysis of query performance over time, enabling more informed decisions for future query optimization and resource allocation.

3 FIG. 300 302 shows a flowchart of an example methodfor dynamically optimizing SQL query execution in a Snowflake environment, according to some embodiments. In step, SQL queries may be submitted to Snowflake using a Python framework (sometimes referred to as SnowLoadBalancer). This framework may act as an intermediary layer between a user's application and Snowflake, managing query submission and/or resource allocation.

304 In step, the system may generate an EXPLAIN plan for the SQL query and analyze the SQL query to determine a warehouse size based on analysis and complexity of SQL. The EXPLAIN plan is a detailed breakdown of how Snowflake will execute the query, including information about table scans, joins, and other operations. The system may use the EXPLAIN plan on SQL and/or use query statistics of same (or similar) SQLs previously run (e.g., using a query identifier). This historical data can help in making more accurate predictions about resource requirements. The system may collect the total number of bytes scanned and/or number of different operator types (e.g., Aggregator, Joins, Filters) used. Each 250 micro partitions (1 micro partition may handle 16 MB compressed data) may, for example, correspond to one core (e.g., considering node is equivalent to 8 cores, 200 GB disk or 1 XS Warehouse). For each operator type, two additional cores may be used. For example, for four different operators, 4 times 2, equal to 8 cores may be used. To illustrate, suppose a query needs to scan 4000 MB of data, that would be equivalent to 250 micro partitions (4000 MB/16 MB per partition), corresponding to one core. Further suppose this query also uses three different operator types (e.g., a JOIN, an AGGREGATE, and a FILTER), it would require an additional 6 cores (3 operators times 2 cores per operator). In total, this query may require 7 cores.

306 Based on Snowflake processing and various types of loads, the system may, in step, select a warehouse size as follows:

Calculated Calculated Warehouse Number Cores> Cores<= Size of Cores >0  <=4 XS 8 >4  <=12 S 16 >12  <=28 M 32 >28  <=60 L 64 >60  <=91 L 64 >91 <=124 XL 128 >124 <=186 XL 128 >186 <=248 XL 128 >248 <=310 XL 128 >310 <=372 2XL 256 >372 <=434 2XL 256 >434 <=496 2XL 256 >496 <=558 2XL 256 >558 <=620 3XL 512 >620 <=682 3XL 512 >682 <=744 3XL 512 >744 <=806 3XL 512 >806 <=868 3XL 512 >868 <=930 3XL 512 >930 <=992 3XL 512 >992 <=1054  3XL 512

This table may provide a mapping between the calculated core requirements and the appropriate Snowflake warehouse size. The table may help ensure that queries are allocated sufficient resources without over-provisioning.

For an SQL, suppose there are 16,000 micro partitions. Suppose operator types are CREATE TABLE, Result, CreateTableAsSelect, Aggregate, Join, UnionAll, Generator, JoinFilter, SemiJoin, TryDeduplicate, TableScan, and Filter. Calculated cores may be (16,000/250)+(12 times 2)=64+24=88 (L Warehouse). For this example, 16,000 micro partitions/250 micro partitions per core=64 cores for data processing. 12 operator types times 2 cores per operator results in 24 cores for operators. So total cores needed may be 64+24, equal to 88 cores. According to the example table shown above, 88 cores falls in the range for an L warehouse (between 60 and 91 cores).

308 In step, for a selected warehouse size, the system may choose the least busy warehouse among the warehouses of same size to avoid unnecessary queue time. The process may include: fetching the current status of the warehouses, filtering the warehouses which are matching with chosen warehouse size, collecting the information on current usage on these warehouses (e.g., number of queries running, number of queries in queue), and/or choosing the warehouse with least (number of queries in running) value.

For example, suppose there are three L-sized warehouses available: warehouse L1: queries running, 2 in queue; warehouse L2: 3 queries running, 0 in queue; and warehouse L3: 4 queries running, 1 in queue. The system may choose warehouse L2 as it has the least number of running queries and no queue.

310 In step, if selected warehouse size is not available, the system may provision a new warehouse (e.g., based on the warehouse size selected) and select that new warehouse. The process may, for example, include fetching the current status of warehouses, filtering the warehouses which are matching with the chosen warehouse size, and/or collecting the information on current usage on these warehouses (e.g., number of queries running, number of queries in queue). If all the warehouses in select size of warehouse have queries in queue, then the system may create a new warehouse and submit SQL on the new warehouse. This dynamic provisioning may help ensure that queries are not delayed due to lack of resources. For instance, if all L-sized warehouses are at capacity or have queries in queue, the system may automatically create a new L-sized warehouse to handle the incoming query.

312 In step, the system may submit the SQL on the chosen warehouse and collect query_ID. This query_ID may be a unique identifier assigned by Snowflake to track the execution of the query.

314 316 In stepsand, the system may monitor the query_ID until success/failure. This continuous monitoring allows for real-time tracking of query progress and enables quick identification of any issues that may arise during execution.

1 2 The system may handle parallelism by dividing the SQLs into multiple jobs, which runs Python Process of SnowLoadBalancer), each jobs may run multiple SQLs. The jobs may be triggered at the same time in parallel. In each job, the above checks may happen for each SQL before it gets submitted to a snowflake warehouse. As the jobs are running in parallel, for one or more jobs, the latest information on warehouse may be collected from Snowflake for better load balancing across warehouses. This may in turn help in spreading the load across warehouses where high, medium, or less complex SQLs may be distributed across all the warehouses. For example, if there are 100 SQL queries to be executed. The system may divide these into 10 jobs, each handling 10 queries. Each job runs independently, performing the warehouse selection and query submission process for its assigned queries. As Jobis selecting a warehouse for its first query, Jobmay be doing the same for its third query, and so on. This parallel processing ensures that warehouse utilization information is always up-to-date, leading to optimal load distribution.

300 300 The methodmay run either on Snowflake metadata service layer or in a Python script in the system. The methodmay take milli-seconds to few seconds (e.g., less than 5 seconds) to complete, according to some embodiments. This rapid execution may help ensure that the optimization process incurs minimal overhead to the overall query execution.

In one aspect, a method is provided for dynamically optimizing query execution in a distributed computing environment. The distributed computing environment may include a cloud-based data warehousing system with separated compute and storage architectures (e.g., Amazon Redshift with RA3 instances using S3 for storage). In traditional data warehousing systems, compute and storage resources are tightly coupled. Separated compute and storage architectures may decouple these resources, offering several benefits. This approach, for example, allows for independent scaling of compute and storage based on workload demands, and may lead to improved cost-efficiency as users only pay for the compute resources they use, while storage can be optimized separately. Separated compute and storage architectures enables concurrent access, where multiple compute clusters can access the same data simultaneously, and facilitates data sharing, allowing different compute environments to access shared datasets without data movement. For example, in a system like Snowflake, data is stored in cloud object storage, while virtual warehouses (compute clusters) are spun up on-demand to process queries. Multiple warehouses can access the same data concurrently, and compute resources can be easily scaled up or down without affecting data storage. This architecture allows for more flexible and efficient query optimization strategies, as compute resources can be dynamically allocated based on query complexity and data volume without being constrained by storage considerations.

1 FIG. 110 112 114 As explained above in reference to, the distributed computing systemmay perform this method. The query reception modulemay receive a query for execution (e.g., SQL query: “SELECT SUM(sales) FROM transactions WHERE date >=‘2023-01-01’”). The query analysis modulemay then analyze the query to determine its complexity. This analysis may include generating an execution plan for the query (e.g., query plan showing table scan, filter, and aggregation operations), determining a total data volume to be processed in the query (e.g., 500 GB of transaction data), and/or identifying types and number of operations to be performed (e.g., 1 table scan, 1 filter, 1 aggregation).

The query plan showing, for example, table scan, filter, and aggregation operations, may correspond to a sequence of steps that a database management system would take to execute the given SQL query efficiently. A table scan is typically the first step in the query execution. The database engine may read all rows from a transactions table sequentially. During this process, the database engine may examine every record in the table, which can be time-consuming for large datasets. This operation is necessary because the query may need to consider all transactions to calculate the total sales, for the example above. After the table scan, a filter operation may be applied. In this case, the filter corresponds to the WHERE clause in the SQL query: “WHERE date >=‘2023-01-01’.” This step narrows down the dataset by only keeping the records that meet the specified condition. The filter eliminates all transactions that occurred before Jan. 1, 2023, for example, significantly reducing the amount of data for subsequent operations. A final step may be the aggregation operation, which corresponds to the SUM function in the SQL query. This operation may calculate the total sales by adding up the ‘sales’ values from all the records that passed through the filter. The aggregation operation is an example of a summary statistic that condenses multiple rows of data into a single result. These operations work together to efficiently process the data and produce the desired result. The query plan helps the database optimizer understand how to best execute the query, potentially using indexes or parallel processing to improve performance, especially when dealing with large volumes of data like the 500 GB mentioned in the example.

116 The resource allocation modulemay then determine required computational resources based on the analysis. This determination may include estimating a number of processing units needed based on the total data volume (e.g., 20 cores for 500 GB), estimating additional processing units needed based on the types and number of operations (e.g., 2 cores for aggregation), and/or calculating a total number of processing units required (e.g., 22 cores total).

118 Next, the execution environment selectormay select an execution environment for the query. This selection may include identifying available execution environments capable of providing the required computational resources (e.g., warehouses of size medium and large), determining current workload status of the identified execution environments (e.g., medium: 70% utilized, large: 40% utilized), and/or choosing an execution environment with the lowest current workload, based on real-time workload information (e.g., selecting the large warehouse).

210 2 FIG. As described in stepof, if no suitable environment is available or all suitable environments exceed a predefined workload threshold, the system may provision a new execution environment (e.g., creating a new Large warehouse if all existing ones are >80% utilized). For instance, a predefined workload threshold could be set at 80% utilization. If all suitable warehouses exceed this threshold, a new warehouse may be provisioned. No suitable execution environment may be available due to several factors. For example, the factors may include high utilization of existing environments, insufficient computational resources, reaching platform-specific concurrency limits, workload management constraints, misalignment with data distribution strategies, peak demand periods, and/or specialized resource requirements. When all existing environments are operating at or near capacity (e.g., above 80% utilization), lack the necessary resources for a query's complexity and data volume, and/or have reached their query concurrency limits, the environments may become unsuitable. Additionally, workload management features, poor alignment with data distribution, high system load during peak times, or queries requiring specific configurations can also result in a lack of suitable environments. In such cases, the system may automatically provision a new execution environment.

216 218 2 FIG. The query may then be submitted to the selected or provisioned execution environment (e.g., sending the SQL to the chosen Large warehouse). As outlined in stepof, the system may monitor the query execution until completion (e.g., tracking progress, runtime, and resource usage). Finally, as described in step, the system may update a query performance database with execution statistics (e.g., storing query runtime, bytes processed, and CPU usage).

In some embodiments, the system may perform load balancing across multiple execution environments to optimize overall system performance (e.g., distributing queries across three medium warehouses instead of using one large warehouse). Load balancing across multiple execution environments can be achieved using various algorithms. These may include, for example, round robin, which may distribute queries sequentially across available environments; least connections, which may send queries to the environment with the fewest active connections; weighted round robin, which may assign weights to environments based on their capacity and distributes queries accordingly; and dynamic least connections, which may be similar to least connections but may also consider the current load and capacity of each environment. For instance, a weighted round robin approach may be used to ensure that environments with higher capacity receive proportionally more queries, for efficient utilization of resources.

When provisioning a new execution environment, the system may either create a new compute cluster or scale an existing compute cluster according to specific scaling mechanisms of the distributed computing environment (e.g., in Redshift, adding nodes to an existing cluster instead of creating a new one).

As illustrated in the parallel processing example described above, the system may handle multiple queries concurrently. The system may repeat the process of receiving the query, analyzing the query, determining the required computational resources, selecting the execution environment, submitting the query, monitoring the query execution, and/or updating the query performance database, for multiple queries in parallel. Each query's execution environment selection may be based on real-time workload information (e.g., processing 10 concurrent queries, each potentially using different warehouses based on current load).

The parallel processing of multiple queries may include dynamically adjusting execution environment selections based on changing workload conditions (e.g., redirecting a new query to a Medium warehouse after a long-running query on a Large warehouse completes).

As explained above, the system may dynamically adjust execution environment selections based on platform-specific concurrency limits of the distributed computing environment. For example, in Snowflake, redirecting queries to less busy warehouses when approaching the query concurrency limit (e.g., 8 queries) of a Standard edition virtual warehouse. In Amazon Redshift, for example, the system may consider the maximum concurrent queries limit, which may vary based on the cluster node type and size. The distributed computing environment may be a cloud-based data warehousing system (e.g., Snowflake, Amazon Redshift, or Azure Synapse Analytics). The execution environments may be virtual warehouses with varying computational capacities (e.g., Snowflake's X-Small to 4X-Large warehouses), as described above in reference to the example warehouse size selection table.

114 When analyzing the query, the query analysis modulemay examine statistics from previous executions of similar queries (e.g., checking average runtime and resource usage of similar aggregation queries on the transactions table).

116 The resource allocation modulemay determine the required computational resources based on a mapping of data volume and operation complexity to processing units (e.g., core per 25 GB of data, plus 2 cores for each join operation).

The system may perform the provisioning of a new execution environment automatically without user intervention (e.g., system automatically creates a new Large warehouse when needed, without requiring admin approval). This automatic provisioning may be performed in accordance with the specific scaling mechanisms of the distributed computing environment (e.g., in Azure Synapse, automatically scaling compute resources based on Data Warehouse Units (DWUs)).

When monitoring query execution, the system may collect performance metrics for future optimization (e.g., recording query runtime, CPU usage, memory usage, and I/O operations for each executed query). These performance metrics may be specific to the distributed computing environment (e.g., in Redshift, collecting queue time and disk-based queries metrics).

114 When analyzing the query to determine its complexity, the query analysis modulemay consider data distribution strategies and sort keys specific to the distributed computing environment (e.g., in Redshift, checking if the query benefits from the current distribution key of the transactions table). For instance, in Amazon Redshift, the system may consider the distribution style (e.g., EVEN, KEY, or ALL) and sort key of tables when analyzing query complexity.

116 The resource allocation module, when determining required computational resources, may consider the impact of data distribution strategies on resource requirements (e.g., estimating lower resource needs for a query that aligns well with the current data distribution).

118 The execution environment selector, when selecting an execution environment for the query, may consider platform-specific workload management features of the distributed computing environment (e.g., in Azure Synapse, considering the impact of workload management groups on query prioritization).

The execution environments may be one of: virtual warehouses, compute clusters, or parallel processing units with varying computational capacities (e.g., Snowflake virtual warehouses, Redshift clusters, or Azure Synapse SQL pools).

The system's load balancing across multiple execution environments to optimize overall system performance may take into account platform-specific workload management capabilities of the distributed computing environment (e.g., in BigQuery, distributing queries across multiple reservations while considering slot commitments). In Snowflake, for example, this may include considering resource monitors and warehouse sizing automation. In Azure Synapse, for example, this may include evaluating workload importance and isolation settings.

The distributed computing environment may be one of: a multi-cluster shared data architecture, a massively parallel processing (MPP) system, or a distributed SQL query engine (e.g., Snowflake as a multi-cluster shared data architecture, Redshift as an MPP system, or Presto as a distributed SQL query engine). In a multi-cluster shared data architecture like Snowflake, multiple compute clusters can simultaneously access the same data storage, allowing for efficient scaling and workload isolation.

116 The resource allocation modulemay determine the required computational resources based on a mapping of data volume and operation complexity to platform-specific compute units of the distributed computing environment (e.g., in Azure Synapse, mapping query complexity to required Data Warehouse Units (DWUs)).

4 FIG. 400 400 410 420 430 440 420 422 410 illustrates an example of a computer systemon which embodiments described herein may be executed. The computer systemincludes a processor, memory, storage, and network interface. The memorystores program instructionswhich, when executed by the processor, perform the methods described in this disclosure.

4 FIG. 400 Embodiments described herein may be executed on one or more computer systems, which may interact with various other devices. One such computer system is illustrated by. In different embodiments, computer systemmay be any of various types of devices, including, but not limited to, a personal computer system, desktop computer, laptop, notebook, or netbook computer, mainframe computer system, handheld computer, workstation, network computer, a camera, a set top box, a mobile device, a consumer device, video game console, handheld video game device, application server, storage device, a peripheral device such as a switch, modem, router, or in general any type of computing or electronic device.

400 410 420 430 400 440 430 450 460 470 480 480 450 400 400 700 In the illustrated embodiment, computer systemincludes one or more processorscoupled to a system memoryvia an input/output (I/O) interface. Computer systemfurther includes a network interfacecoupled to I/O interface, and one or more input/output devices, such as cursor control device, keyboard, and display(s). Display(s)may include standard computer monitor(s) and/or other display systems, technologies or devices. In at least some implementations, the input/output devicesmay also include a touch- or multi-touch enabled device such as a pad or tablet via which a user enters input via a stylus-type device and/or one or more digits. In some embodiments, it is contemplated that embodiments may be implemented using a single instance of computer system, while in other embodiments multiple such systems, or multiple nodes making up computer system, may be configured to host different portions or instances of embodiments. For example, in one embodiment some elements may be implemented via one or more nodes of computer systemthat are distinct from those nodes implementing other elements.

400 410 410 410 410 410 In various embodiments, computer systemmay be a uniprocessor system including one processor, or a multiprocessor system including several processors(e.g., two, four, eight, or another suitable number). Processorsmay be any suitable processor capable of executing instructions. For example, in various embodiments, processorsmay be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In multiprocessor systems, each of processorsmay commonly, but not necessarily, implement the same ISA.

410 In some embodiments, at least one processormay be a graphics processing unit. A graphics processing unit or GPU may be considered a dedicated graphics-rendering device for a personal computer, workstation, game console or other computing or electronic device. Modern GPUs may be very efficient at manipulating and displaying computer graphics, and their highly parallel structure may make them more effective than typical CPUs for a range of complex graphical algorithms. For example, a graphics processor may implement a number of graphics primitive operations in a way that makes executing them much faster than drawing directly to the screen with a host central processing unit (CPU). In various embodiments, graphics rendering may, at least in part, be implemented by program instructions configured for execution on one of, or parallel execution on two or more of, such GPUs. The GPU(s) may implement one or more application programmer interfaces (APIs) that permit programmers to invoke the functionality of the GPU(s).

420 410 420 420 425 435 420 400 400 430 440 System memorymay be configured to store program instructions and/or data accessible by processor. In various embodiments, system memorymay be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory. In the illustrated embodiment, program instructions and data implementing desired functions, such as those described above for scaling computing clusters in distributed systems as described herein are shown stored within system memoryas program instructionsand data storage, respectively. In other embodiments, program instructions and/or data may be received, sent or stored upon different types of computer-accessible media or on similar media separate from system memoryor computer system. Generally speaking, a computer-accessible medium may include storage media or memory media such as magnetic or optical media, e.g., disk or CD/DVD-ROM coupled to computer systemvia I/O interface. Program instructions and data stored via a computer-accessible medium may be transmitted by transmission media or signals such as electrical, electromagnetic, or digital signals, which may be conveyed via a communication medium such as a network and/or a wireless link, such as may be implemented via network interface.

430 410 420 440 450 430 420 410 430 430 430 420 410 In one embodiment, I/O interfacemay be configured to coordinate I/O traffic between processor, system memory, and any peripheral devices in the device, including network interfaceor other peripheral interfaces, such as input/output devices. In some embodiments, I/O interfacemay perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory) into a format suitable for use by another component (e.g., processor). In some embodiments, I/O interfacemay include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example. In some embodiments, the function of I/O interfacemay be split into two or more separate components, such as a north bridge and a south bridge, for example. In addition, in some embodiments some or all of the functionality of I/O interface, such as an interface to system memory, may be incorporated directly into processor.

440 400 400 440 Network interfacemay be configured to allow data to be exchanged between computer systemand other devices attached to a network, such as other computer systems, or between nodes of computer system. In various embodiments, network interfacemay support communication via wired or wireless general data networks, such as any suitable type of Ethernet network, for example; via telecommunications/telephony networks such as analog voice networks or digital fiber communications networks; via storage area networks such as Fibre Channel SANs, or via any other suitable type of network and/or protocol.

450 400 450 400 400 400 700 440 Input/output devicesmay, in some embodiments, include one or more display terminals, keyboards, keypads, touchpads, scanning devices, voice or optical recognition devices, or any other devices suitable for entering or retrieving data by one or more computer system. Multiple input/output devicesmay be present in computer systemor may be distributed on various nodes of computer system. In some embodiments, similar input/output devices may be separate from computer systemand may interact with one or more nodes of computer systemthrough a wired or wireless connection, such as over network interface.

4 FIG. 420 425 435 425 425 435 As shown in, memorymay include program instructions, configured to dynamically optimize query execution in a distributed computing environment as described herein, and data storage, comprising various data accessible by program instructions. In one embodiment, program instructionsmay include software elements of embodiments as described herein and as illustrated in the Figures. Data storagemay include data that may be used in embodiments. In other embodiments, other or different software elements and data may be included.

400 400 Those skilled in the art will appreciate that computer systemis merely illustrative and is not intended to limit the scope of the stereo drawing techniques as described herein. In particular, the computer system and devices may include any combination of hardware or software that can perform the indicated functions, including a computer, personal computer system, desktop computer, laptop, notebook, or netbook computer, mainframe computer system, handheld computer, workstation, network computer, a camera, a set top box, a mobile device, network device, internet appliance, PDA, wireless phones, pagers, a consumer device, video game console, handheld video game device, application server, storage device, a peripheral device such as a switch, modem, router, or in general any type of computing or electronic device. Computer systemmay also be connected to other devices that are not illustrated, or instead may operate as a stand-alone system. In addition, the functionality provided by the illustrated components may in some embodiments be combined in fewer components or distributed in additional components. Similarly, in some embodiments, the functionality of some of the illustrated components may not be provided and/or other additional functionality may be available.

The dynamic query execution optimization system described in this disclosure offers several advantages over traditional approaches. By analyzing queries in real-time and dynamically allocating resources, it can significantly improve query performance and system efficiency. The ability to provision new execution environments on-demand ensures scalability and prevents bottlenecks during peak usage periods. Moreover, the continuous monitoring and collection of performance data allow for ongoing optimization of the system. This adaptive approach ensures that the system can evolve and improve over time, adjusting to changing workloads and query patterns.

While the present disclosure has been described with reference to various implementations, it will be understood that these implementations are illustrative and that the scope of the disclosure is not limited to them. Many variations, modifications, additions, and improvements are possible. More generally, embodiments in accordance with the present disclosure have been described in the context of particular implementations. Functionality may be separated or combined in procedures differently in various embodiments of the disclosure or described with different terminology. These and other variations, modifications, additions, and improvements may fall within the scope of the disclosure as defined in the claims that follow.

The foregoing description, for purpose of explanation, has been described with reference to specific implementations. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The implementations were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various implementations with various modifications as are suited to the particular use contemplated.